In the past decade, there has been a marked increase in the development, calculation and use of summary measures of population health, which combine information on mortality and non-fetal health outcomes. This study seeks to improve the conceptual, methodological and empirical basis for the calculation of summary measures. The starting point will be the evaluation of different summary measures along a set of basic criteria, such as the requirement that a summary measure should improve if, for example, age-specific mortality in a population declines, ceteris paribus. New population-based data will be collected on one of the critical inputs to all summary measures of health, name valuations of health states worse than perfect health. Valuations of a range of different states, along with a rich set of data on self-reported health status in various domains general health status, socio-demographic characteristics and, through record linkage, , medial diagnoses and treatment outcomes, will be collected in a nationally-representative sample survey in Denmark. Analyses will be undertaken on the relationships between health state evaluations and performance in various domains of health. A critical component of the project will be multi-variate analyses to examine how valuations on different health domains may vary as a function of age and other socio- economic variables. The national survey in Denmark will also provide a link to the project on comparing self-reported and observed measures on health status, allow estimation of the relationships between self-reported health and health state valuations. The findings from the Denmark study will inform the next phase of this project, which is a series of pilot tests in field sites in Tanzania and Mexico with the objectives of extending health state valuation protocols to developing countries. Using the data on health state valuations and epidemiological data produced by the WHO Global Burden of Disease 2000 Network, various summary measures will be computed. Simulation algorithms will be developed to estimate confidence intervals around summary measures as a function of uncertain epidemiological and preference inputs.